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Algorithms 2014, 7(3), 328-338; doi:10.3390/a7030328
Article

Economic Dispatch Using Modified Bat Algorithm

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Received: 17 April 2014; in revised form: 11 June 2014 / Accepted: 25 June 2014 / Published: 3 July 2014
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Abstract: Economic dispatch is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents an over view of three optimization algorithms namely real coded genetic algorithm, particle swarm optimization and a relatively new optimization technique called bat algorithm. This study will further propose modifications to the original bat. Simulations are carried out for two test cases. First is a six-generator power system with a simplified convex objective function. The second test case is a five-generator system with a non-convex objective function. Finally the results of the modified algorithm are compared with the results of genetic algorithm, particle swarm and the original bat algorithm. The results demonstrate the improvement in the Bat Algorithm.
Keywords: economic dispatch; genetic algorithm (GA); particle swarm optimization (PSO); bat algorithm (BA); modified bat algorithm (MBA); non-convex optimization economic dispatch; genetic algorithm (GA); particle swarm optimization (PSO); bat algorithm (BA); modified bat algorithm (MBA); non-convex optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Latif, A.; Palensky, P. Economic Dispatch Using Modified Bat Algorithm. Algorithms 2014, 7, 328-338.

AMA Style

Latif A, Palensky P. Economic Dispatch Using Modified Bat Algorithm. Algorithms. 2014; 7(3):328-338.

Chicago/Turabian Style

Latif, Aadil; Palensky, Peter. 2014. "Economic Dispatch Using Modified Bat Algorithm." Algorithms 7, no. 3: 328-338.


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